2022 Fiscal Year Final Research Report
Evaluation of various methods of phenotyping from the health insurance claims data
| Project/Area Number |
17K09226
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Medical and hospital managemen
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| Research Institution | International University of Health and Welfare (2019-2022)
The University of Tokyo (2017-2018) |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
中島 直樹 九州大学, 大学病院, 教授 (60325529)
森田 瑞樹 岡山大学, ヘルスシステム統合科学研究科, 教授 (00519316)
佐藤 真理 順天堂大学, 大学院医学研究科, 特任助手 (90768631)
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| Project Period (FY) |
2017-04-01 – 2023-03-31
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| Keywords | レセプト分析 / Phenotyping / バリデーション / データ分析基盤 |
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| Outline of Final Research Achievements |
We have developed an implementation of a method to keep a high level of personal information protection in joint data analysis projects consist of multiple medical institutions. This is done by creating a database of medical data within each medical institution based on a common standard that is used internationally. This method is also compatible with the processed pseudonymized information that recently introduced under the Personal Information Protection Law. The advantage of this method is that it allows for the analysis of data from multiple medical institutions while avoiding the rounding of information through anonymization. It has also led to the establishment of a framework for research promotion far beyond our initial expectations, including the development of collaboration with an international research community consisting of several thousand people.
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| Free Research Field |
医療情報学
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| Academic Significance and Societal Importance of the Research Achievements |
機微性の高い患者データを院外に出さずに、個人情報保護法の制度に準じながら高い精度のデータを複数医療機関共同で安全に分析することができるようになった。国際的な共同研究も患者情報を国外に出さずに実施できるため、世界との協調が容易である。これにより日本の研究機関もガラパゴス化に陥ることなく医療データ分析を進めることができ、医療データ分析分野に大きく寄与すると共に、分析結果である多数の医療エビデンスにより、より良い医療に貢献できる。
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